基于人工智能的肺病学评估 住院患者咨询说明完整性:预测文档缺口和响应延迟
Damla Azakli Yazici1, Celal Satici2, Ayse Bahadir1
1Basaksehir Cam and Sakura City Hospital, Department of Pulmonology, Istanbul, Turkey.
International journal of medical informatics
|August 3, 2025
概括
不完整的住院咨询笔记显著延迟了患者的护理. 使用自然语言处理 (NLP) 的人工智能 (AI) 工具可以实时识别缺失的信息,以改进文档.
科学领域:
- 医疗信息学 医疗信息学
- 改善临床文档 改善临床文档
- 医疗保健中的人工智能
背景情况:
- 住院咨询笔记往往缺乏完整性,阻碍了临床决策和患者护理协调.
- 缺乏标准化的文档框架,影响了多学科沟通.
- 不完整的笔记可能导致咨询响应时间的延迟.
研究的目的:
- 评估转诊医生文件完整性与咨询响应时间之间的关联.
- 开发一种人工智能 (AI) 工具,用于实时检测咨询说明中缺少的信息.
主要方法:
- 使用修改后的QNOTE框架,对1219份住院患者咨询笔记进行了回顾性分析.
- 评估缺少的临床要素与咨询响应时间之间的关系.
- 开发基于自然语言处理 (NLP) 的机器学习模型,以识别缺失的内容.
主要成果:
- 只有0.3%的笔记包含了所有基本组件;缺少的信息与响应延迟高达35分钟有关.
- 文件完整性和响应时间因班次和日期而异,晚上和周末的表现更好.
- 在识别缺失的元素方面,NLP模型表现出高准确度 (F1分数>0.90).
结论:
- 不完整的参考说明与延迟的咨询响应有显著的联系,突出了需要改进文档的需要.
- 一个人工智能辅助的NLP模型可以从自由文本笔记中实时检测缺失的专业特定内容.
- 这种方法可扩展到其他专业,为提高住院患者咨询效率提供了切实可行的解决方案.
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